数据集获取

git clone https://github.com/honglizhan/CovidET-Appraisals-Public.git

curl -L -o repo.zip https://github.com/honglizhan/CovidET-Appraisals-Public/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

CovidET-Appraisals (EMNLP 2023 Findings)

This repo contains the dataset for our EMNLP 2023 findings paper. If you use this dataset, please cite our paper. We publicly release our annotated dataset CovidET-Appraisals, model outputs, and our human evaluation data here.
Title: <a href=“https://aclanthology.org/2023.findings-emnlp.962/”>Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models</a>
Authors: <a href=“https://honglizhan.github.io/”>Hongli Zhan</a>, <a href=“https://cascoglab.psy.utexas.edu/desmond/”>Desmond C. Ong</a>, <a href=“https://jessyli.com/”>Junyi Jessy Li</a>

@inproceedings{zhan-etal-2023-evaluating,
    title = "Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models",
    author = "Zhan, Hongli  and
      Ong, Desmond C.  and
      Li, Junyi Jessy",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    monevent-blocked= dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.962",
    pages = "1441814446",
    abstract = "The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005). Thus, the same situation can often result in different emotional experiences. While the detection of emotion is a well-established task, there is very limited work so far on the automatic prediction of cognitive appraisals. This work fills the gap by presenting CovidET-Appraisals, the most comprehensive dataset to-date that assesses 24 appraisal dimensions, each with a natural language rationale, across 241 Reddit posts. CovidET-Appraisals presents an ideal testbed to evaluate the ability of large language models {-} excelling at a wide range of NLP tasks {-} to automatically assess and explain cognitive appraisals. We found that while the best models are performant, open-sourced LLMs fall short at this task, presenting a new challenge in the future development of emotionally intelligent models. We release our dataset at https://github.com/honglizhan/CovidET-Appraisals-Public.",
}

Abstract

The emot

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

完整仓库：github.com/honglizhan/CovidET-Appraisals-Public